---
title: "COVID19 - New Zealand"
author: "Matt Bixley"
output:
flexdashboard::flex_dashboard:
orientation: rows
source_code: embed
vertical_layout: fill
theme: bootstrap
#logo: images/coronavirus.png
---
```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
library(tidyverse)
library(lubridate)
library(ggthemes)
library(scales)
#theme_set(theme_wsj)
#theme_set(theme_bw)
#------------------ Parameters ------------------
# cases today
nzcases = 85
nzdeaths = 0
#------------------ Data ------------------
source("code/import_jhu.R")
source("code/insert_today.R")
#source("code/population.R")
# read the jhu set for the lat/long
jhu_confirmed <- "https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv"
jhu <- read_csv(jhu_confirmed) %>%
rename(country = "Country/Region") %>%
map_df(str_replace_all, pattern = " ", replacement = "_") %>%
mutate(country = ifelse(country == "Taiwan*", "Taiwan",country)) %>%
mutate(country = ifelse(country == "Korea,_South", "South_Korea",country)) %>%
mutate(country = ifelse(country == "US", "United_States_of_America",country)) %>%
group_by(country) %>% # hack to get just one copy of country
mutate(first = rank(country)) %>%
filter(first == 1) %>%
select(country, Lat, Long)
# Import from the European CDC
ecdc_url <- "https://opendata.ecdc.europa.eu/covid19/casedistribution/csv/"
download.file(url = ecdc_url, destfile = "data/ecdc.csv")
covid19 <- read_csv("data/ecdc.csv") %>%
rename(date = "dateRep", country = "countriesAndTerritories", population = "popData2018") %>%
left_join(.,jhu) %>% # merge with jhu to get the lat long
mutate(date = dmy(date)) %>%
arrange(date, country) %>%
mutate(population = ifelse(country == "China", 58500000/0.83, population)) %>% #correct pop to reflect hubei
group_by(country) %>%
mutate(cum_cases = cumsum(cases)) %>%
mutate(cum_deaths = cumsum(deaths)) %>%
ungroup() %>%
mutate(country = ifelse(country == "United_States_of_America", "USA",country)) %>% # per capita
filter(country != "Cases_on_an_international_conveyance_Japan")
covid19 <- insert_today(cases = nzcases, deaths = nzdeaths)
covid19 <- covid19 %>%
mutate(per_million = round(cum_cases/population*1000000,1))
latest <- covid19 %>% filter(date == max(date))
nz <- covid19 %>% filter(country == "New_Zealand")
usa <- covid19 %>% filter(country == "USA")
italy <- covid19 %>% filter(country == "Italy")
```
Sidebar {.sidebar}
=====================================
{ width=100% }
Summary
=======================================================================
Row {data-width=400}
-----------------------------------------------------------------------
### confirmed {.value-box}
```{r}
#valueBox(value, subtitle, icon = NULL, color = "aqua", width = 4,href = NULL)
valueBox(
value = paste(format(max(nz$cum_cases), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = "silver"
)
```
### active {.value-box}
```{r}
valueBox(
value = paste(format(max(nz$per_million), big.mark = ","), "", sep = " "),
caption = "Cases per million",
icon = "fas fa-user-md",
color = "silver"
)
```
### death {.value-box}
```{r}
valueBox(
value = paste(format(max(nz$cum_deaths), big.mark = ","), "", sep = " "),
caption = "Death cases (death rate)",
icon = "fas fa-user-md",
#color = ifelse(nz$cumulative_deaths > 0, "red", "silver")
color = "silver"
)
```
Row
-----------------------------------------------------------------------
### **Daily cumulative cases by type** (New Zealand only)
```{r}
# plot nz growth
#who <- "New Zealand"
who <- c("New_Zealand", "Australia", "USA", "Italy", "China", "South_Korea")
p1 <- covid19 %>% filter(country %in% who, per_million > 5) %>%
group_by(country) %>%
mutate(days5 = dense_rank(date)) %>%
ungroup() %>%
#ggplot(aes(x = date, y =cum_cases , colour = country)) +
ggplot(aes(x = days5, y = per_million, colour = country)) +
geom_point() +
geom_line()
p1 + theme_wsj() + scale_colour_wsj()
```
Comparison
=======================================================================
Column {data-width=400}
-------------------------------------
### **Daily new cases**
```{r}
#----------------------------------------
# Plotting the data
who <- c("New_Zealand", "Australia", "USA", "Italy", "China", "South_Korea")
p2 <- covid19 %>% filter(country %in% who , cum_cases > 0) %>%
group_by(country) %>%
mutate(days = dense_rank(date)) %>%
ungroup() %>%
ggplot(aes(x = days, y = cum_cases, colour = country)) +
geom_point() +
geom_line() +
scale_y_continuous(trans = "log10",
breaks = trans_breaks("log10",function(x) 10^x), labels = trans_format("log10", math_format(10^.x)))
p2 + theme_wsj(color = "gray") + scale_colour_wsj("colors6")
```
### **Daily Cases**
```{r}
who <- c("New_Zealand", "Australia", "USA", "Italy", "China", "South_Korea")
p3 <- covid19 %>% filter(cum_cases > 360) %>%
ggplot(aes(x = date, y = country, fill = cases)) +
geom_tile(colour = "lightgray") +
scale_fill_gradient(low = "orange", high = "orangered4")
p3 + theme_wsj(color = "gray", ) + scale_colour_wsj("colors6") +
theme(axis.text.y = element_text(size = 5))
```
Map
=======================================================================
### **World map of cases** (*use + and - icons to zoom in/out*)
```{r}
# make a map
#library(leaflet)
#library(leafpop)
#map_object %>%
# addLayersControl(
# overlayGroups = names(cv_data_for_plot.split),
# options = layersControlOptions(collapsed = FALSE)
# )
```
About
=======================================================================
**The Coronavirus Dashboard: the case of New Zealand**
This Coronavirus dashboard: the case of New Zealand provides an overview of the 2019 Novel Coronavirus COVID-19 (2019-nCoV) epidemic for New Zealand. This dashboard is built with R using the R Makrdown framework and with Flexadashboard.
**Code**
The code behind this dashboard is available on [GitHub](https://github.com/mattbixley/covid19){target="_blank"}.
**Data**
The input data for this dashboard is the dataset available from the
[{ width=3%}](https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide){target="_blank"} European Centre for Disease Prevention and Control
The data and dashboard are refreshed on a daily basis.
Some data is also pulled from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/CSSEGISandData/COVID-19){target="_blank"}.
But I find them late to update due to time zone differences.
**Contact**
For any question or feedback, you can email me or report an issue at the [GitHub repo](https://github.com/MattBixley/COVID19/issues){target="_blank"}
**Update**
The data is as of `r format(max(covid19$date), "%A %B %d, %Y")` and the dashboard has been updated on `r format(Sys.time(), "%A %B %d, %Y")` at `r format(Sys.time(), "%H:%M")`.
European Centre for Disease Prevention and Control